A Building Management System or “BMS” is traditionally used to orchestrate the operation of the climate control infrastructure (which may include sensors, A/C vents, chillers etc.) in large buildings. It is essentially a control centre that takes inputs from sensors and is programmed to activate various appliances in response to the value of certain variables (such as temperature, humidity, etc.) deviating from a target set-point, in accordance with a predefined policy. This policy can be designed to maximise comfort, minimise cost or, as is usually the case, achieve a suitable compromise between both objectives.
However, conventional BMS suffer from a number of potential problems.
One issue is the poor placement of sensors or, more specifically, a poor mapping between sensors and actuators. For instance, an A/C vent may be opened in response to a temperature reading from a sensor that is not located in its optimal area of influence. The result could be that the air-conditioning system brings the temperature down to uncomfortable levels in one zone while attempting to lower it in another (where the sensor is located). Conversely, at other times, the system could let the temperature drift too high above the set-point in its area of influence because the sensor reading (from another location) mistakenly indicates that it is within tolerance limits. Note that this pathological behaviour does not result from an easily identifiable fault in the system but from poor planning, or perhaps post-installation modification of the building or the system which can be much harder to detect or remedy.
Another, closely related, problem with similar consequences presents itself when the sensor is suitably located with respect to the climate control appliance it triggers, but near an independent source that is not controlled (or monitored) by the BMS (for example a heat or cold source, but also a source of humidity such as a kettle. A typical example is a temperature sensor located downstream from a server ventilation system, which can lead to the ambient temperature in the room being consistently and severely overestimated.
Another area for potential improvement is flexibility. Because it is centrally controlled, informed by a fixed set of sensors and triggered at arbitrary set-points, a conventional BMS usually does not take into account individual preferences. This means that the temperature can be maintained, sometimes at considerable cost, above or below what the occupants of a particular zone actually find comfortable, which is obviously wasteful.
Finally and more generally, centralised BMSs are an aging technology that embodies a “command and control” approach to the management of a large but static climate control infrastructure that is expected to remain in use and virtually unchanged for decades. As a result, conventional BMS design is simply unfit to deal with the unpredictable and dynamic “proliferation” of a wide range of sensors and smart appliances that will develop in the Internet of Things (IoT) environment.
Jazizadeh et al. [1] describes approaches to facilitate the communication between humans and buildings toward adaptive end-user comfort management and, specifically, how occupants communicate their comfort preferences to the building management systems. An interaction framework is set out in the paper to enable occupants to control the systems to meet their comfort requirements whilst reducing energy consumption. The human building interaction (HBI) framework described employs a participatory sensing approach to improve comfort level, energy awareness and learning in commercial buildings. Both mobile and web-based applications were provided to capture participants' comfort levels in terms of temperature, light intensity and air flow. The collected data was then analyzed and compared with the actual sensor readings in an attempt to meet occupant preferences and reduce energy consumption.
The system described is based on the interaction between humans and buildings to achieve energy reduction and improve comfort levels.
Fortino and Guerrieri [2], [3] propose an agent-based architecture to decentralize the sensing and actuation operations of a BMS. It was achieved through distributed cooperative agents embedded in sensor/actuator devices and coordinators such as PCs, PDA and smartphones. The communication between different types of agents is either peer-to-peer or master/slave dependent on which type of agent is interacted with which. The system was deployed in a computer laboratory where the sensing devices were used to collect the information about ambient light, user presence and electricity consumed by the workstations. The collected information was analyzed to monitor the space occupancy and the pattern of energy consumption.
Although both papers provide a decentralized approach to monitor the power usage, the exact sensor positions need to be defined in advance which is not conducive to a system which can adapt to the addition or removal of sensors.
US 2011/0178977 A1 describes a method of analysing faults in a building management system. The method detects a fault in the building management system by evaluating data from the building management system using a system of rules. It then determines the most likely cause of fault by comparing the determined probabilities of previously identified faults based on the application of the Bayes' theorem and reports the most likely fault electronically. Although this system is designed to improve building efficiency and provide more comfortable and productive buildings, the main focus is on fault identification by standard machine learning means.
U.S. Pat. No. 8,600,556 B2 provides a very detailed description of a smart building manager which aims to improve building efficiency. The building manager consists of many layers such as communications and integrated control layer, demand response layer and building subsystem integration layer to collect and process information, and determine the outputs. A fault detection and diagnostics layer is also included in the building manager to detect and diagnose faults based on statistical analysis, rule-based analysis, and model-based analysis.
This smart building manager is based on a centralised approach to collect and process information. It is therefore not suitable for a distributed system.
U.S. Pat. No. 7,567,844 B2 describes a building management system which can handle one or more buildings which may be located at different places in the world. The system provides a 3 dimensional or 3-dimensional render in 2 dimensional model of the building being monitored. A variety of building management or control devices including sensors, actuators, chillers, steam plants, security systems, smoke detectors, and lighting systems are employed to monitor and control the subject building by a central building controller. In addition, the actual locations and status of the sensors and control devices, treated as points of interest, can be mapped on the virtual model of the building. Information related to the building or buildings can be recorded and played back as needed for analysis.
The main focus of the above patent is a method for presenting a 3 dimensional model of a building with the data and locations of control devices mapped to the model. Thus it provides a human manager of the building with an opportunity to identify problematic areas showing inefficient resource consumption, but requires human input to deal with those problems.
An object of the present invention is to provide a climate control system and a control method which are able to adjust relationships between sensors and climate control devices.
A further object of the present invention is to provide a climate control system and a control method which can take account of sensors which provide inaccurate feedback.
A further object of the present invention is to provide a climate control system and a control method which is flexible to individual preferences.
A further object of the present invention is to provide a climate control system and a control method which is flexible to the addition and/or removal and/or relocation of sensors and climate control devices.